A system and method are disclosed for short term forecasting of power requirement and specifically for short term load forecasting in a distribution network of a power grid.
Typically, a power grid includes a generation system for generating electric power, a transmission system for transmitting the generated power and a distribution system for distributing the transmitted power. While transmission systems are designed to carry giga watts of power, distribution systems are configured to handle only tens of megawatts of power. Power grids are evolving with deployment of distributed generation techniques that add small quantum of power using smaller power generating centers from multiple locations. Power grids are also increasingly adopting smart technologies leveraging digital communication technology to reduce cost, save energy, and increase reliability.
Forecasting power requirements for an electrical grid is a complex task. A forecast that underestimates demand may result in a brownout or a blackout. A forecast that overestimates demand may result in generation of unused power at considerable expense. Short-term load forecasting refers to estimating the power requirements on an hourly basis ahead of one or more days. Estimation and prediction techniques for short-term forecasting are based on modelling of system loads as a function of weather parameters using statistical techniques. Unfortunately, known technology does not provide precise near-term real-time load forecasts. System operators make critical operating decisions such as selection of generation unit to be dispatched to meet system demand in short span of time. Next-day and near-term load forecasts are often made by system operators as the day unfolds based on the demand data of the previous day. In most cases, these real-time adjustments are not very helpful to determine accurate next-day forecast.
An energy management system (EMS) and a distribution management system (DMS) are important components of a smart grid. EMS and DMS are utilized for providing capabilities to the smart grid to operate the bulk power system in a safe, reliable, and economic manner. Further EMS and DMS are used for developing new functions and capabilities in the smart grid for improving reliability and efficiency of the distribution system of the smart grid. Typically, in the smart grid, the EMS includes load forecasting methodologies for the transmission system and the DMS uses load forecasting methodologies for the distribution system. Generally, estimated load forecasting errors for the transmission system are lower due to stable operating power conditions, whereas estimated load forecasting errors for the distribution system are higher due to large power requirement variability. In some conventional systems, static load profiles (or load shapes) based approaches are used to roughly estimate the short-term load demand in the DMS. Load forecasting approaches based on a consumption pattern in a similar scenario are used when an operator is allowed to build and modify forecasts using historical power grid data. Load forecasting approaches of this type which need human intervention can be time consuming. Further, human intervention is difficult to quantify and requires a certain amount of expertise.